{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The importance of inductive bias in convolutional models for statistical downscaling\n",
    "\n",
    "***9th International Workshop on Climate Informatics 2019 (Paris, France)***\n",
    "\n",
    "**J. Baño-Medina and J.M. Gutiérrez**\n",
    "\n",
    "http://dx.doi.org/10.5065/y82j-f154\n",
    "\n",
    "GitHub repository at https://github.com/SantanderMetGroup/DeepDownscaling\n",
    "\n",
    "This notebook reproduces the results published in the [conference paper](https://github.com/SantanderMetGroup/DeepDownscaling/blob/master/2019_Bano_CI.pdf) titled *The importance of inductive bias in convolutional models for statistical downscaling by J. Baño-Medina and J. M. Gutiérrez, in the 9th International Workshop on Climate Informatics held in Paris in 2019. In particular, the code developed herein delves into the influence of multi-site convolitional topologies and its regularization properties. For the sake of comparison we compare this multi-site conv models with equivalent single-site architectures and classical statistical downscaling methods i.e., generalized linear models (GLM) and analogs.\n",
    "\n",
    "Note: This notebook is written in the free programming language R and builds on the R framework [climate4R](https://github.com/SantanderMetGroup/climate4R) (C4R hereafter, conda and docker installations available), a suite of R packages developed by the [Santander Met Group](http://meteo.unican.es/en/main) for transparent climate data access, post processing (including bias correction and downscaling, via the downscaleR package; [Bedia et al. 2020](https://gmd.copernicus.org/articles/13/1711/2020/gmd-13-1711-2020-discussion.html)) and visualization. The interested reader is referred to [Iturbide et al. 2019](https://www.sciencedirect.com/science/article/abs/pii/S1364815218303049?via%3Dihub)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Loading Libraries\n",
    "In this study, we build on C4R, which is used for loading and post-processing, downscaling, validation and visualization. Different sectorial [notebooks](https://github.com/SantanderMetGroup/notebooks) are available illustrating the use of C4R functions. The C4R libraries that are needed to run this notebook can be installed through the devtools package (e.g. devtools::install_github(\"SantanderMetGroup/loadeR\") for loadeR); see detailed instructions [here](https://github.com/SantanderMetGroup/climate4R). The deep learning models used in this work are implemented in [downscaleR.keras](https://github.com/SantanderMetGroup/downscaleR.keras), an extension of downscaleR which integrates keras in the C4R."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(loadeR) # version 1.6.1 \n",
    "library(transformeR) # version 1.7.4\n",
    "library(downscaleR) # version 3.1.3\n",
    "library(visualizeR) # version 1.5.1\n",
    "library(climate4R.value) # version 0.0.2 (also relies on VALUE version 2.2.1)\n",
    "library(downscaleR.keras) # version 1.0.0 (relies on keras version 2.2.2 and tensorflow version 2.0.0)\n",
    "\n",
    "library(magrittr)\n",
    "library(gridExtra) # for plotting functionalities\n",
    "library(RColorBrewer) # for plotting functionalities\n",
    "library(sp) # for plotting functionalities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Loading Data\n",
    "In this section we describe how to load into our R session the predictor and predictand datasets involved in this study; ERA-Interim, and E-OBS (version 14), respectively. In this study we rely on the [User Data Gateway (UDG)](http://meteo.unican.es/udg-tap/home), a THREDDS-based service from the Santander Climate Data Service (CDS) to load the data into our session (register [here](http://meteo.unican.es/udg-tap/signup) freely to get a user). We can log in using the `loginUDG` function from loadeR:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "loginUDG(username = \"***\", password = \"***\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We find the label associated to ERA-Interim via the `UDG.datasets` function of library `loadeR`: **“ECMWF_ERA-Interim-ESD”**  (type `?UDG.datasets()` for information on the full list of datasets available at UDG). Then we load the predictors by calling `loadGridData` of `loadeR`. In particular, we load the Iberia region (IP) defined in the Prudence regions, which are stored as an object in library `visualizeR`. We then use the `lonLim` and `latLim` arguments to define the domain and `years` to load daily data for the period 1979-2008.\n",
    "\n",
    "We loop over the `loadGridData` function to load a different predictor variable at each iteration. Since we rely on the perfect-prognosis approach of statistical downscaling models, we load those predictor variables that are reliably represented by GCMs (despite the fact in this study we only work in the observational space with reanalysis data): geopotential, specific humidity, air temperature, and the zonal and meridional wind velocities at 500, 700, 850 and 1000hPa. The code of these variables can be found by calling `C4R.vocabulary` of `loadeR`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data(\"PRUDENCEregions\", package = \"visualizeR\")\n",
    "bb <- PRUDENCEregions[\"IP\"]@bbox\n",
    "lonLim <- bb[1,]\n",
    "latLim <- bb[2,]\n",
    "\n",
    "variables <- c(\"z@500\",\"z@700\",\"z@850\",\"z@1000\",\n",
    "               \"hus@500\",\"hus@700\",\"hus@850\",\"hus@1000\",\n",
    "               \"ta@500\",\"ta@700\",\"ta@850\",\"ta@1000\",\n",
    "               \"ua@500\",\"ua@700\",\"ua@850\",\"ua@1000\",\n",
    "               \"va@500\",\"va@700\",\"va@850\",\"va@1000\")\n",
    "x <- lapply(variables, function(x) {\n",
    "  loadGridData(dataset = \"ECMWF_ERA-Interim-ESD\",\n",
    "               var = x,\n",
    "               lonLim = lonLim, \n",
    "               latLim = latLim,  \n",
    "               years = 1979:2008)\n",
    "}) %>% makeMultiGrid() %>% redim(drop = TRUE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Regarding the predictand dataset, we use `loadGriData` to load into our R session the total daily rainfall amount (coded as `pr` in `C4R` vocabulary, see `?C4R.vocabulary()`) from E-OBS (version 14) at 0.5º resolution. Again, this dataset is stored in the UDG, and its label can be obtained by calling `UDG.datasets()`.\n",
    "We call `binaryGrid` function of `transformeR` to convert to 0 all the daily values lower or equal than 1 mm/day. Therefore, according to this criteria, we define a rainy day whenever the rain is greater than 1mm/day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# UDG.datasets(\"E-OBS\") # Uncomment to obtain the label of E-OBS version 14\n",
    "y <- loadGridData(dataset = \"E-OBS_v14_0.50regular\",\n",
    "                   var = \"pr\",lonLim = lonLim,\n",
    "                   latLim = latLim, \n",
    "                   years = 1979:2008) %>% binaryGrid(condition = \"GT\", threshold = 1, partial = TRUE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next figure we observe the Iberian Region, as defined by the Prudence regions, which is indeed our domain of study, and the predictor and predictand spatial resolutions, 2º and 0.5º, respectively. This figure can be created by using the function `spatialPlot` of `visualizeR`, and introduce the grids' coordinates as an spatial points object using the function `SpatialPoints` of base libraty `sp`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2021-01-08 13:05:33] - Computing climatology...\n",
      "\n",
      "[2021-01-08 13:05:33] - Done.\n",
      "\n"
     ]
    },
    {
     "data": {
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jObzY899tipU6eio6O9vLi4CeCINBPs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Caubk5J4997cx2MmRw0w1RRU3ryOqpnlm\nEpWfTHXkCGSiasrxZMr6AonacPPj6kQdY2d+eJmoQ83kyGGmmqKKy7Hh5sfVGZfv3c+Z/s3F\nGpXs5gR5Wlo+0SCH6XNSjl2pyj7YS6+mJpqUqaYosv5m6o6aTp5Q60fJuejY9IzMJg3/GbGT\nNduptqCk7vglX0FJI6+4JMO2CzxnwkTgqQMmsmY7UeTYcPNsl5aZ+/raS+XtrBYOqmVjXYKp\nK+Gfw2r9YNdkTU00qaGaOqWmYKdWp87EVK9aqWqV4pxcBgClJTM7b9b6v9Oz8j4eVbucrWrm\nYQAogY+Awp06E9O4IVewA6BGeXmGRT/EJdzNXDLM26U8f6sDZR3BrnAnI2KaNMh/SiwAKM5g\nkD74Of7U3/ffH+Zd1cVW6XYAKI9gV4i8PENE1EXuOQFAhVbuStgXee+9IbU8q9gr3QsAVWDc\nvhAXL19NTU1v5O+jdCPyysrOWbZq6+nIy77ebvV8a9at7V7Xx93engEAQL02ht388fDt9172\nrlvDQeleAKgFwa4QJ89EV3St4OFeVelGZHTkeNT46UvuJCY/90yr46dj1v/w+63bSZaWFp7u\nT/0T8mobo16NcuXslG4WgCRJ0o5jd1fvuT73Rc+m3o5K9wJARQh2hTgVEdP0oXtO6EZaembw\n0nXLV4Y817XVkvmvVnJ1Mj6elHz/ctyNc9FXzsfER124svWXg7HxNw0GQ9UqrvV8a9ar7W4M\nfA39vMo7MAEElLYD55I+2hY/pVeNDn7OSvcCQF0IdoU4eSY6oEk9pbuQRfih0xNf/yg7O2fz\nN//t1OqB8OpcoXyTBt5NGnibHjGPeueir/z2+zGiHqCIE5dTF2yKHdml2nMBlZTuBdCenIz0\nzOR7IgvmqusaewS7ghgMhtORF0cMflbpRgRLSk59e9GX32z8ddjAHotmj3F0LJedUcg9jB+O\neskpaZdirxca9RrU93QsX07mDQLKiovX0+ds/Pu55pUGdnhK6V4AqBHBriCxV64n3ktpoq+p\n2F93H5oya3m5cnbbv1vcoU3jwr/hMSo4ORD1gNJ09W7mjLWX2tStMLFHiW8FC0CnCHb/cnR/\nJt8jF/76sUKFCg3aDrG0FHNdGIPBcO3aterVq1tYlOCeP08qISFhwoQJv/zyyxtvvDFr1iw7\nO8FnQrhKkmdT6WmzR+7duxcVFRUZGXn27NnIyMgde3fEx8dLkuTp6Vm/fv0GDRqY/uvk5FRo\n/cSYzWIbloOlrQxHshc2nqoSbSbvE1tw3ztNxBaUSee3T5bCT4mPjx/Wrl3HLs9u3rzZ2pqP\nbgCPxqdDQY4fP96kSZMSprobN278ZebevXtVqlTp0qVLYGBgYGCgp6enqG4LYDAYvvzyy9df\nf71OnTpHjx5t2LBhKfxQSZJcXFzatGnTpk0b0yNJSUlRUVFnzpyJioqKiIjYtGlTXFycJEme\nnp716tUzj3oVKlQonSYB9btz5063bt1q1ar13XffkeoAFIAPiIIcP368WbNmxf2utLS048eP\nG2PcoUOHYmNjy5cv36xZs5YtW44aNap+/fpHjx4NDQ2dPXv2qFGjfH19jQnv6aefdnV1lWMr\nYmJiRo8effjw4f/+97+vvfaalZWVHD+liJydnVu3bt26dWvTI8nJyaaod+bMmc2bN8fGxkqS\n5OHhUb9+fX9/f2PUa968uXJdA0pKTU3t2bOnnZ3dTz/9ZG/PyUkACkKwK8jx48cHDhxY6Gp5\neXlRUVGmJHfmzBmDwVC/fv2WLVvOmjWrVatW/v7+5n9kN2jQYPjw4QaD4cyZM6GhoaGhocOG\nDUtPTw8ICDCGvLZt2wqZJ83JyVm6dOmcOXPatWsXERHh7e1d+PeUugoVKrRq1apVq1amR1JS\nUsyj3g8//BAbGztt2rTZ49oUUAfQpaysrL59+yYmJoaFhTk7c3ETQCeWL1++bNmy+Pj4WrVq\nvfXWW0OGDHl4nV69em3fvt38kTFjxqxYsaLgygS7x0pISLhx48bjRuwSEhL++uuvw4cPHz58\n+OjRoykpKTVq1GjVqtXAgQNbtmzZvHlzR8dCjrWysLBo2LBhw4YNp06dmp2dffDgQWPIW7x4\nsa2tbYcOHYwhr3Hjxk92QN6pU6dGjRp18eLFTz/9dPjw4Yoc1fdknJycWrZs2bJlS9MjGzdu\nHDVq1JggvyqV2bGhDMnNzX355ZcjIyMPHDhQtaqeL5MOlCmrVq2aPn36woULW7VqtXfv3mHD\nhjk7O/fu3TvfaikpKb179546darpETc3t0KLE+we69ixYw4ODnXr1jX+8/79+ydOnDj2P2fP\nnnV0dGzcuHFAQMDw4cPbt29fkvEwGxubjh07duzYccGCBffv3zeGvO++++7111+vVKnS008/\nHRgY2LVrVy8vr6JUy87O/vDDD+fMmdO1a9ctW7a4u7s/cWMq8eKLLy5YsOCT1T/Pn/mIv2kA\nvZo2bdquXbv2799fxPc+APUzGAyLFi2aMGHCjBkzJEnq2LFjVFTUwoULHxnsAgICOnfuXKz6\nBLvHOnHihJub25o1aw4fPvzXX3+dPXvWwsKiQYMGrVq1+r//+79WrVrVr19f1Nmy5sqXL28c\nq5MkKT4+3jiMN3fu3DFjxhTlgLzw8PBXX301KSlp48aNffv2Fd6eIqysrN56660xo0dNerVP\n5YqcVIEyYfbs2atXr969e3fjxk9+WSIAahMdHR0bG9unTx/TI7169RoyZEhycnK+swaTk5ML\nnf17mPhcohsXL16MiYlZuHBhSkrKsGHD9u3bl5SUdOLEiRUrVowYMcLf31+OVJePu7v78OHD\n161bl5CQcPr06XHjxsXGxg4bNqxKlSqtWrV66623fv/998zMTOPKKSkpEydO7NSpU7t27c6e\nPaubVGc0cODA6lUrfvbVNqUbAUrD0qVLFy9evHnzZvOTygHowIULFyRJ8vHxMT1iXI6Ojs63\nZkpKSvny5YtbnxG7x1q+fPnixYtVclxLoQfktW7des2aNdbW1rt27erSpYvS/YpnZWU1dVzf\nNxZ8NXFU74ouhV/0DtCub7/9dvr06d9++2337t2V7gVAkbz99tuVKj1wlz9LS8s33nijSZP8\n1+NMTk6WJMl8cM54JVfj4+ZSUlKOHDnSunXryMjIatWqBQUFvf322+XKFXKFf4LdYzk7O6vz\nHDTzA/KSk5N///330NDQLVu2BAUFLViwwMHBQekG5fJinw5LPtm8Ys2OWVMHKN0LIJft27eP\nHDly2bJlRTklH0BxZWdnp6WlCyxovFesk5NTvkOkLC0tS3KBi7y8PFtb2ytXrkyfPt3NzS08\nPHz+/PlxcXHr1q0r+BsJdtpWoUKFPn36mE/V65i1ldXUsS/Mfveb8SN6uTgXe3QaUL9Dhw4N\nGDDgrbfemjBhgtK9ACiG119/vW3btkVZ08XFRZKkpKQk0+DRvXv3TI+bWFpaJiYmmv7Ztm1b\ng8HwxhtvLFu2LN/QYD4cYwctGdC3c0UXp5Xf7FC6EUC8iIiInj17Dhs2bO7cuUr3AkAuxqtt\nmB9Rd/78eSsrK9NVOB7HeB6V8c6cBVDTiJ35hdYMBsE1hRcsazXV0aSNtdVro/vMf3/92OHP\nOld4xKCdq2+QaTkxOqTELUqSJDnW7GpcSL2yS0hBh6r//lWXduNPITXl2HDPDvNMy7Fh8x67\nXrGIfkt2nnPKtLxvgZizR+WoWehv+6VLl7p169a9e/ePP/642DVV+7khd01NNKmVmppo8vE1\nU1NTr1y5cu3atZSUFDE/VDY+Pj6+vr5btmwxHRC/devWTp065TuS6vz582+++eY777zj7+9v\nfOTgwYNWVla1a9cuuL5qgp0cl881r2lhIew3TNaaQsh9LWJFN/zloC5LV279Yt3O6eP75fuS\nebgRxZTqjMuisp2JQ9W2orKdWOapThiZ35Kd55wSlsPEKuwtefPmzR49evj7+69Zs6aop9vL\n/fkmBB/sApX5JzNXkm5IUvxff127ds2Y4eLj4xMSEhISEq5cuWLMc9bW1oWeW6AGs2fPHjly\npLu7e5s2bbZv3/7LL7/s2bPH+KXPPvtsw4YN4eHhXl5eERER/fr1++9//+vm5vbHH38sXrx4\nypQphZ4nq5pgV2Zp54YQ8iry+9/G2mriyN7By74fO6ynY3kNvIGhLebDdaUjKSmpe/furq6u\nW7ZsEXIvQTFUm2+ga+np6deuXUtISDD999J77yVI0jVJipOkHEmSWrWyt7d3c3OrXr26m5ub\nn59fYGCgt7e38Z8eHh5PcIf30jd06NDU1NQlS5bMmTPH19d306ZNpqsQx8XFHTp0SJIkOzu7\n3bt3z5o167XXXrt9+7aHh0dwcPDEiRMLLa6aYGcwaC/iCPnUk2PD5X4y5fi4L07NoS91Wbpy\ny5frfpsy5nnzxxOjQ+QYtJOVOofrJEmKDZsny6CdnIQM1+1b0Fh8tnv8WzI9Pb1Xr17Z2dmh\noaFPcCXSf+sLp9rPN7mpNs7q6MlMTEz8J7RduvRAhrt0yXS6gKur6z9xbfRov1Wr3CSpuiS5\nSZJPYmK+kww0avz48ePHj3/48eDg4ODgYOOyl5fXhg0biltZNcFOMvutFfW+MtYR+3en8Ca1\nUlNNTdrZ2kwc1fuDT394dUj38g725l8yZTtRx5kZ517FTsKm3fjTeJidwFQnfMMls2wn7AA7\nGd6SphwmcBJWjpqP/G3Pzs4OCgr6+++/Dxw4ULFixZIXlKNJ1dXkg11gTdmezHRJunbxYkJ4\nuHloMy7ExcXl5ORIkmQ+8Obv7x8YGGj6p4eHh7W1WT5ZuVL8k6lfagp2kor/7pS1oFZqqqnJ\nVwY8s2zV1q827Jo0Kv/N9QQmGxPhh9bJMVBXwg2PjL4Wdh39mckAABflSURBVOSSrY2VvZ2N\nJEmWlhZO5e2kBUGSJDkejrGyspQkydbGqpy96av/RGpHB1vjV9PS0op0GUXRv0hyHFdX3Jqp\nqanZ2dmmf+bl5SUlJZmvkJaWlnn0qCRJ0rFjxkcSExO/+uqro0ePhoWF1axZ80m6VNNbUvM1\nNdGkymomJibmG3Iz/veii8u9e/ckHx/JfODNzc3Pz88U3Xx8fIo38EakKzKVBTugaOztbV8b\n1WfZF1tHDe5WrpxqDkvSpuu3kpd8sffH307V8XrKxsYqLSMrOztXkqTsnLy09CzjOsmp6UX4\nXH3P+H8WFhamj2xHR0cbGxtJkuzs7Iyx75FftbW1NR0RbLrI562jN6wtLSRJsrayKGf7zykF\naZl5uXn/tpJnkO5n5po3kZmdl5Vj1qtBSs14YIWsnLzMnAc2JjUj12C2eTm5hvSsPPMV0jLz\n8sxWyM0zpGWarTDnCSfIKlasGBoa6uvr+2TfDsjtEUe8FWfgzdPT08rKSumNKHMIdtCq4QOf\nWbZq69ff7R73ynNK96JV6RnZKzceWLXhTx/PyhuXDWvVxKvQbzHFvpycvPv/xr4Mg8FQvcno\ntLQ0482Lc3JyTBcduHfvnjE2PfKrSUlJeXl5kiSlp6dnZGRIknTr1i3jVxP/vn8/I9eYqDKz\nDTm5hvL2D5wxamdjaWv9wCNO9g/sRWysLexsLB3L/fugo72V+XFK1pb/RkYjBztLK8t/17C0\ntHCwe2CFcjaW1lb/rmBhYdFp4vfmK9jb2+c7L8/FxcXC7Keap1hAcY8deLt40XjhXOlRA2/G\nf7q7u6vzFk1lGcEOWuVQzm78iOeWffHTKwO72tvbKt2OxhgMhl/2nV302e6cnNzZk7q99FxT\nq6JdZcPB3lb632GNlVwfSCeeAQFim9z3Tv57LKpTgOgNB8QyH3jLl+EYeNMfgh007NUhPT5d\nvW3tpj2jh/ZQuhctOXYqeua8r6Jirg/v12ri0A7lHZjLBnTixo0b27dvv3DhQkJCQnx8vPGS\nb2lpaZIk2draVqtWzd3d3c3NrUaNGi1atHBzc6tZs6Zx4E0Tl38TIisrJy0tU2BB471i1YNg\nBw1zKGc3ZtizH63YMuylQDs7G6Xb0YD4hNsLP9oY8nNYj05+H8/tV6OaHq4aAODixYtbt27d\nsmXLwYMHq1Sp0rRp0+rVq3fs2LF69eo1atRwd3evXr161apVLTR3wRQUH8EO2jZ6aI/Pvtq2\n/offRwzqWvjaZVhS8v2lK7eu+GZHw/q1fv3uv3WrZxf+PQDULTIycvv27du2bfvzzz89PT17\n9+49b968zp07P3CtEJQxvPbQNifHcmOGP/vRih9fDvqPrQ2/z4+Qk5u7PuT3RUu/s7Oz+eid\nMS8939HCwiL5eqTSfQF4Enl5eSdOnNi2bdvGjRsvXLjg5+fXq1ev4ODgdu3aMSAHiWAHHRg7\nrOeKr7dv/GHfsAGBSveiOvv/PP3Wom+uXrszZczzY4c/a2fLhDWgSZmZmWFhYdu2bQsJCblx\n40bTpk0HDhw4aNCgOnXqKN0a1IVgB82r4OQwekiPDz7/YWC/zgzamVy4eHVO8Nq9YacGBz29\n5ZsBVSpxSQJAe9LS0vbs2RMSEvLzzz9nZGR06NBh5syZL774YvXq1ZVuDSrFXhB6MO6V51au\n/TXkpz8G9/+P0r0o7+69lPc/3vzl+p3tWzXY//Pi+nU8lO4IQPHcuXNnx44dISEhu3fvtrKy\n+s9//rN8+fI+ffpw0TgUimAHzXCt3f+xX5KkCRMvLP3y+7HTlxfrqOG0mwdFtPYvh6faiC0o\nSZLDU0VdMysr6/PPP587d66bm9vPP2/r2bPn49Ys4MlUj85vn1S6BeARjn31vPCadk6ukiQl\n3ErdeyRu/7H4I2evOznYdGjqvmRK+7aN3GxtrCTp9yu7fr9SnJpO1b3ENmltX0FsQUmSHFye\n6H56j5eXnSq2oOYQ7KAT06ZN+/jjjzds2DB06FCle1HGtm3bJk+enJKSMn/+/IkTJ3JBUUAr\nLiWkHDwXv+/olZMXblav7Ph085ojn2/Qwq+a8S7MQLEQ7KATlSpVGjdu3DvvvDNo0KCydqr/\nkSNHpk2b9tdff40dO/add96pUEH8X9UAxMozGM7HJYWdvP7bX1djr6f6uLt0Cqg5ZXBA07pV\nObcVJVG29n/QtxkzZnz22Wfff//94MGDle6llMTHx8+aNWv9+vX9+vU7d+5crVq1lO4IQEHy\n8gynL94NPZqw52jCnaTMuh7O3VrW6NbKvY6v4BlJlFkEO+hH5cqVx4wZs3DhwoEDB1oW7c6n\n2nX//v33339/8eLFDRo02L9/f/v27ZXuCMBjZWTl/hV1K/RIwh8nr6dn5jTwdh3awzewuVsV\nF/vCvxkoDoIddGXGjBmff/55SEjISy+9pHQvcsnLy1u3bt3MmTOtra1XrFgxZMgQrkoKqFNS\nalb46Rthp64fiLhhMEgt6leZMahhp6bVHMtxRUnFZGXl3Bd7r9g87hULyKZq1aqvvvrqf//7\n36CgIF0O2u3evfv//u//YmNj33zzzSlTptjb8+c+oDrX7qTtO3E97NT1Y+duOzrYtGtYde6I\nZu0aVi1nxylNkB3BDnrz+uuvf/XVV6NGjVq1apWezqKIioqaMWPGzp07R44cuXv37qpVqyrd\nEYAHXEpICTt1/Y+T109fvFutokOnptWG9/ANqFfZypIxdZQe/ez2ACM3N7fff/+9Z8+effv2\n/f7778uVK6d0RyV19+7dxYsXf/TRRx06dDh27Fjjxo2V7gjAP0wnt+46cvXva6nebk4dGleb\n1N+vce1KHCIBRRDsoEMBAQFhYWHdunXr2rXrtm3bXFxclO7oye3Zs6d///7Vq1ffsmVLARcc\nBlCaHj65tWuLGl1buntVd1S6NZR1BDvoU506dQ4cONCtW7eOHTvu3LnTzc1N6Y6exO7du/v0\n6TNu3Lj33ntPT9PKgHZFRUXNXnUs/PSNzOzcFvUqv9q7buem1StWsFO6L+AfKju6XI6Ra03U\ntLCQpaZwmtpwNze3sLAwZ2fndu3anT9/viQlHaq2FdHZgwrb8F27dvXp02fChAkffPBBkVJd\nmf0tkqNmmX0yy+yGF6FmTk7Ou+++26xZs7spmW8OabR7afflU9v07eT1uFQXMPKngJE/ie2x\nwYtfiy0oSZJnh3nCa9ZoMU1sQVffIFffILE19UpNYwDGN5XxvwYRJw+b3qUy1RRSUNaaZX7D\nXVxcQkNDBw0a1LZt2x07drRu3bq49UyRzriQduNPYU1KBW34zp07X3jhhUmTJi1evFhUzWLj\nLamVJ7PMbrgcNR9TMCIiYsSIETExMcuWLQuw/qXQkqZIFzDyp2Or+who8n+pzvjfM5uGl7yg\nKdIZF2LD5pW8pinS1Wgx7eqRD0teUJIkU6Rz9Q1KjA4RUlPHVDNip8WjTIX0rJW/ZeWuL9uT\naWdnt2nTpr59+wYGBv72228CfopYj+r5119/feGFFyZPnlzUVCcH3pIC8ZbUkId6zs7Ofu+9\n95o3b16tWrUzZ86MHj1akb7kGKuTm/BxOxSFmkbsyiaDQZOffcKJGh54FCsrq1WrVtWoUaN3\n795r165V+bWLf/nll379+k2ePDk4OFjpXsok3pJGcr4lNeTQoUMjR468ffv2F198MXToUKXb\nAQqnmhE7OT5EzGvKXV9V5G5MgxtuYWExb968xYsXDx48+KOPPip6SfO5VzHzsPk82POOHTv6\n9u37xhtvKJ/qeEsKxFtSpppy1k9PT3/jjTfat2/v7+8fGRmpeKoTMveaj/ncq5B52HxETcWi\nWNQ0YqeJD3pNNClHTU00WVjNyZMnV6pUacSIETdu3Ch6chKf5x7T5Pbt2/v37z9r1qw5c+aI\nqlkimqipiSblqKmJJrVS86GC4eHho0aNSk5O3rx58/PPP/8EJUUdV2dO7mwnhBxhjuPqikVN\nwQ6Q38svv1ytWrUXXnjh5s2bq1atUrqdf23evHnQoEFz5syZPXu20r0AZVdaWtqCBQuWLFky\naNCgZcuWubq6Kt0RBMvKzkkTfK9YgcUEUM1ULFBaAgMD9+zZs23btqCgoIzMLKXbkSRJCgkJ\nGTRo0Ny5c0l1gIJ27txZv379TZs27dy5c+3ataQ6aBHBDmVRy5Yt9+/ff+zYsecHTEtJua9s\nM5s2bRo0aNC8efPeeustZTsByqx79+6NGTPm2Wef7d69++nTpwMDA5XuCHhCBDuUUX5+fmFh\nYddv3u7Rb9Kt24lKtfH999+//PLL77///qxZs5TqASjjtm/f3qBBg7179+7du3flypWOjtwW\nDBpGsEPZ5enp+fuOVbY21k/3fPXi5fjSb+C7774zpropU6aU/k8HcPPmzaFDh77wwgsvv/zy\nmTNnOnXqpHRHQEkR7FCmubpU2BayrLaPxzO9x50+E12aP3rjxo1Dhgz58MMPJ0+eXJo/F4BR\nSEiIv7//qVOnDh48GBwcbGfH/V6hB3KdFWtR4BU+Daq96pIq8WQKVMCT2abLsF0/fd6udeNS\naOOrr74aPXr00qVLJ06cWAo/Tib8ZgrEkylKUZ7Ja9eujR8/fseOHdOmTVuwYIGtrW1pdacx\nXgXeRjaey9SpEiN2wL96Bb22Zdvvcv+U1atXjx49etmyZZpOdYAWGQyGtWvXNmjQ4Pr16ydP\nngwODibVQWe4jh3wr5nTXhk+ds6piJdbt2hYr66XZ83qBf/1/wRWrlw5YcKEFStWjBo1Smxl\nAIXq2rXrgQMH5s6dO336dCsrK6XbAcQj2AH/mjl1uFu1Kiu+2vzJyu/SMzIdy5er6+vl7+dT\nv04t//o+9ep41XB7qiT1P//880mTJq1atWrEiBGiegZQdBkZGSdPnqxTp47SjQByIdgBDxgy\n8NkhA5+VJOna9dvnLlw+e/7yiVPnNm3ZfS54VXpGZoUKjj613OvV8fKr612vrlf9ut61PN2K\nWHnlypWvvfba6tWrhw0bJucWAHis/fv3W1pyDBL0jGAH/MvhqTamZZ+nJJ9G0rP/+2dOTk5c\nXFxkZOTZs2cjIyO3/no4cslXGRkZLi4uPj4+fn5+AQEB/v7+DRpcr1at2sOVV6xYMWnSpC+/\n/JJUByhISKoLGLG15EU0Yl4BX6vRfGpptVEMljYLlG5BYQQ7oEisra29vb29vb179eplfCQr\nK+vcuXNnz56NiIg4e/bsJ598cvny5dzc3GrVqjVo0MDf39/f379BgwZ+fn5fffXV66+//vXX\nXw8ePFjZrQCAMi4rKyctXWTBXJXdK5ZgBzwhW1vbRo0aNWrUaMCAAcZH0tPTo6KiIiMjIyMj\nz5w589NPP8XGxhoMBmtr62+//da0GgAAMiHYAcKUK1euWbNmzZo1Mz2Smpp69uxZe3v7Ro0a\nKdgYAKCMINgBMnJ0dGzZsqXSXQBlQnp6+vz585XuAlCYXMGO66QLxJMpEE+mQDyZAvFkPpk7\nd+6cPXs2Kirq3LlzP/30U3Z29o4dO3r27Kl0XzrBr6UWMWIHANCGuLi4c+fORUVFGZNcZGTk\n7du3LS0tvby86tevP2TIkGnTplWoUEHpNgElEewAAKpjvMDQpUuXTNcYOn36dEpKio2NTc2a\nNf38/Fq3bj1o0CA/P7+mTZuWL19e6X4BtSDYAQAUlpWVFR8fb8pwxtnVtLQ0Ozs7Hx8ff3//\nwMDAyZMne3t7+/v729vbK90vUFLLly9ftmxZfHx8rVq13nrrrSFDhpRkNXMEOwBAqUpKSoqJ\niTEfjTt//nxubq6rq6u3t7efn19QUJCfn5+/v7+Xlxc3ioD+rFq1avr06QsXLmzVqtXevXuH\nDRvm7Ozcu3fvJ1stH4IdAEBGiYmJxgBnSnKXL182GAyurq7G9DZ69Gh/f3/jBcCVbhaQncFg\nWLRo0YQJE2bMmCFJUseOHaOiohYuXJgvsRVxtYcR7AAAwiQkJJhnuIiIiBs3blhbW3t4eBhH\n43r16uXn59e4cWMnJyelmwUUEB0dHRsb26dPH9MjvXr1GjJkSHJysvmpP0Vc7WEEO0DKy8sL\nDg5OTk5+3AoVKlSwsrJ63FcdHBzs7Owe91UbGxtHR8fHfdXCwqJmzZqtWrUqereASmRlZUVH\nRxtPUD179uy5c+fOnTuXnp5ub29fr169evXqdezYccyYMfXq1atTp46tra3S/QKqcOHCBUmS\nfHx8TI8Yl6OjowMCAoq72sOePNjZ2Nh88cUXX3/99RNXAADoT0ZGxsmTJ0+ePKl0IyijCvgr\nIi4uLiIpa+vpLOE/tF27dvkesbCw+PHHH59//vl8jxsHEcxH3Yyj1/kGF4q42sOePNgtWrTo\nxRdffOJvBwAAEMvKyqp169aP++r27duTkpIKmGMxyczMTEpKeuqpp4ryQ48ePRoQEGBhYVH0\nTuTz5MGucuXKgYGBAlsBAACQT/v27eUoW6w45OLiIklSUlKSs7Oz8ZF79+6ZHi/uag/jNHIA\nAIBSUrduXUmSoqOjTY+cP3/eysrK+HhxV3sYwQ4AAKCU+Pj4+Pr6btmyxfTI1q1bO3Xq5ODg\n8ASrPYyzYgEAAErP7NmzR44c6e7u3qZNm+3bt//yyy979uwxfumzzz7bsGFDeHh4wasVgGAH\nAABQeoYOHZqamrpkyZI5c+b4+vpu2rSpc+fOxi/FxcUdOnSo0NUKYGEwGGTrHAAAAKWHY+wA\nAAB0gmAHAACgEwQ7AAAAnSDYAQAA6ATBDgAAQCcIdgAAADpBsAMAANAJgh0AAIBOEOwAAAB0\ngmAHAACgEwQ7AAAAnSDYAQAA6ATBDgAAQCcIdgAAADpBsAMAANAJgh0AAIBOEOwAAAB0gmAH\nAACgEwQ7AAAAnSDYAQAA6ATBDgAAQCcIdgAAADpBsAMAANAJgh0AAIBOEOwAAAB0gmAHAACg\nEwQ7AAAAnSDYAQAA6ATBDgAAQCcIdgAAADpBsAMAANAJgh0AAIBOEOwAAAB0gmAHAACgEwQ7\nAAAAnSDYAQAA6ATBDgAAQCcIdgAAADpBsAMAANAJgh0AAIBOEOwAAAB0gmAHAACgEwQ7AAAA\nnSDYAQAA6ATBDgAAQCcIdgAAADpBsAMAANAJgh0AAIBOEOwAAAB0gmAHAACgEwQ7AAAAnSDY\nAQAA6ATBDgAAQCcIdgAAADpBsAMAANAJgh0AAIBOEOwAAAB0gmAHAACgEwQ7AAAAnSDYAQAA\n6ATBDgAAQCcIdgAAADpBsAMAANAJgh0AAIBOEOwAAAB0gmAHAACgEwQ7AAAAnSDYAQAA6ATB\nDgAAQCcIdgAAADpBsAMAANAJgh0AAIBOEOwAAAB0gmAHAACgEwQ7AAAAnSDYAQAA6ATBDgAA\nQCcIdgAAADpBsAMAANAJgh0AAIBOEOwAAAB0gmAHAACgEwQ7AAAAnSDYAQAA6ATBDgAAQCcI\ndgAAADpBsAMAANAJgh0AAIBOEOwAAAB0gmAHAACgEwQ7AAAAnSDYAQAA6ATBDgAAQCcIdgAA\nADpBsAMAANAJgh0AAIBOEOwAAAB0gmAHAACgEwQ7AAAAnSDYAQAA6ATBDgAAQCcIdgAAADpB\nsAMAANAJgh0AAIBOEOwAAAB0gmAHAACgEwQ7AAAAnSDYAQAA6ATBDgAAQCcIdgAAADpBsAMA\nANAJgh0AAIBOEOwAAAB0gmAHAACgEwQ7AAAAnSDYAQAA6ATBDgAAQCcIdgAAADpBsAMAANAJ\ngh0AAIBOEOwAAAB0gmAHAACgEwQ7AAAAnSDYAQAA6ATBDgAAQCcIdgAAADpBsAMAANAJgh0A\nAIBOEOwAAAB0gmAHAACgEwQ7AAAAnSDYAQAA6ATBDgAAQCcIdgAAADpBsAMAANAJgh0AAIBO\nEOwAAAB0gmAHAACgEwQ7AAAAnSDYAQAA6ATBDgAAQCcIdgAAADpBsAMAANAJgh0AAIBOEOwA\nAAB0gmAHAACgEwQ7AAAAnSDYAQAA6ATBDgAAQCcIdgAAADpBsAMAANAJgh0AAIBOEOwAAAB0\ngmAHAACgEwQ7AAAAnSDYAQAA6ATBDgAAQCcIdgAAADpBsAMAANAJgh0AAIBOEOwAAAB0gmAH\nAACgEwQ7AAAAnSDYAQAA6ATBDgAAQCcIdgAAADpBsAMAANAJgh0AAIBOEOwAAAB0gmAHAACg\nE/8PNJFF7drFCEoAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "areas <- PRUDENCEregions\n",
    "n <- names(PRUDENCEregions)\n",
    "n_regions <- length(n)\n",
    "coords_x <- expand.grid(x$xyCoords$x,x$xyCoords$y) ; names(coords_x) <- c(\"x\",\"y\")\n",
    "coords_y <- expand.grid(y$xyCoords$x,y$xyCoords$y) ; names(coords_y) <- c(\"x\",\"y\")\n",
    "spatialPlot(climatology(y),at = seq(0, 4, 0.1), set.min = 0, set.max = 3, \n",
    "            ylim = c(35.75,44.5), xlim = c(-10.25,3),\n",
    "            backdrop.theme = \"coastline\", \n",
    "            col.regions = colorRampPalette(brewer.pal(n = 9, \"BrBG\")),\n",
    "            sp.layout = list(list(SpatialPoints(coords_x), first = FALSE, \n",
    "                                  col = \"black\", pch = 15, cex = 1),\n",
    "                             list(SpatialPoints(coords_y), first = FALSE, \n",
    "                                  col = \"red\", pch = 16, cex = 0.5)),\n",
    "            colorkey = TRUE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Downscaling\n",
    "In this section we develop the code to build all the downscaling methods intercompared in this study. We build on downscaleR and downscaleR.keras libraries to build the generalized linear models and the nearest-neighbour search (i.e., analogs), and finally the deep learning models, respectively.\n",
    "\n",
    "To validate the models we perform a 5-fold cross-validation, i.e., there are 5 models in total for every downscaling method. In particular we define the following chronological folds and store them in an R object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "folds <- list(1979:1984,\n",
    "              1985:1990,\n",
    "              1991:1996,\n",
    "              1997:2002,\n",
    "              2003:2008)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 Benchmark methods (GLM and analogs)\n",
    "In this subsection we develop the GLMs and analogs downscaling methods building on downscaleR. Due to the discrete-continuous nature of precipitation, we build 2 GLMs --a logistic regression and a Gamma regression with link logarithmic, -- that focus on the prediction of the occurrence of precipitation and on the amount of precipitation, respectively. For each location, we limit the predictor space to its 4 closest gridpoints, to avoid high-dimensionality spaces that lead to overfitting.\n",
    "\n",
    "This 2-phase downscaling is not required for analogs, which basically looks for the closest neighbour based on a moving window of 25 neighbours at every site."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1.1 GLM (Logistic Regression)\n",
    "We use the `downscaleCV` to perform the cross-validation for the logistic regresssion. Note that we specify the folds and predictor configuratios (e.g., standardization) via the `downsaleCV` arguments, type `?downsaleCV` for more information. We define the logistic regression by building a GLM (`method = \"GLM\"`) and familiy binomial with link logit (`family = binomial(link = \"logit\")`). Due to the high-computational cost of building a GLM model for each predictand location, we iterate over the latitude dimension and then bind the results by calling `bindGrid` of `transformeR`.\n",
    "\n",
    "Finally, we store in 2 different objects the probabilities of rain (`glm.ocu`) and the binary precipitation (`glm.bin`) thanks to `subsetGrid` of `transformeR`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_bin <- binaryGrid(y, condition = \"GT\", threshold = 1)\n",
    "glm.ocubin <- lapply(1:getShape(y,\"lat\"),FUN = function(z) {\n",
    "  downscaleCV(x,subsetDimension(y_bin,dimension = \"lat\",indices = z),\n",
    "              folds = folds,\n",
    "              method = \"GLM\", family = binomial(link = \"logit\"),\n",
    "              scaleGrid.args = list(type = \"standardize\"),\n",
    "              prepareData.args = list(local.predictors = list(n = 4, vars = getVarNames(x)))\n",
    "  ) \n",
    "}) %>% bindGrid(dimension = \"lat\")\n",
    "glm.ocu <- subsetGrid(glm.ocubin,var = \"prob\")\n",
    "glm.bin <- subsetGrid(glm.ocubin,var = \"bin\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1.2 GLM (Gamma Regression)\n",
    "We repeat the process as for the logistic regression but changing to a Gamma regression defined as `family = Gamma(link = \"log\")` as one of the `downscaleCV` arguments. To center the Gamma on 0, we substract 1mm to the serie and only train with the wet days at each location. After the prediction we call `gridArithmetics` from `transformeR` to elminate this artificial bias of 1mm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_rest <- gridArithmetics(y,1,operator = \"-\")\n",
    "glm.amo <- lapply(1:getShape(y,\"lat\"),FUN = function(z) {\n",
    "  downscaleCV(x,subsetDimension(y_rest,dimension = \"lat\",indices = z),\n",
    "              folds = folds,\n",
    "              method = \"GLM\", family = Gamma(link = \"log\"),\n",
    "              condition = \"GT\", threshold = 0,\n",
    "              scaleGrid.args = list(type = \"standardize\"),\n",
    "              prepareData.args = list(local.predictors = list(n = 4, vars = getVarNames(x)))\n",
    "  ) \n",
    "}) %>% bindGrid(dimension = \"lat\") %>% redim(drop = TRUE) %>% gridArithmetics(1,operator = \"+\")\n",
    "glm <- gridArithmetics(subsetGrid(glm.bin,var = \"bin\"),glm.amo,operator = \"*\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1.3 Analogs\n",
    "Similar to the GLMs, in this case we change the method to analogs by changin the parameter `method` in `downscaleCV`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ana <- lapply(1:getShape(y,\"lat\"),FUN = function(z) {\n",
    "  print(z)\n",
    "  downscaleCV(x,subsetDimension(y,dimension = \"lat\",indices = z),\n",
    "              folds = folds,\n",
    "              method = \"analogs\", n.analogs = 1,\n",
    "              scaleGrid.args = list(type = \"standardize\"),\n",
    "              prepareData.args = list(local.predictors = list(n = 25, vars = getVarNames(x)))\n",
    "  ) \n",
    "}) %>% bindGrid(dimension = \"lat\") %>% redim(drop = TRUE) \n",
    "save(ana, file = \"analogs.rda\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 Downscaling - Deep Neural Networks\n",
    "In this section we develop the deep learning topologies based on the library `downscaleR.keras`. In particular, we develop the CNN1 model studied in a [recent publication](https://doi.org/10.5194/gmd-2019-278) in the *Geoscientific and Model Development* journal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2.1 CNN single-site (CNN-SS)\n",
    "We build two loops that iterate over the latitude and longitude dimensions, to build single-site versions of the CNN1 model at each latitude-longitude predictand gridpoint. This subsetting is performer with `subsetDimension` of `transformeR`. The CNN1 topology, is defined and stored as a `keras` object in `model`. We then call the function `downscaleCV.keras` of `downscaleR.keras` with the model already defined and optimize it by minimizing the negative log-likelihood of a Bernouilli-Gamma distribution, custom loss function `bernouilliGammaLoss` in `downscaleR.keras`. This optimization phase is done with an Adam optimizer and a learning rate of 0.0001 with an early-stopping criteria of 30 apochs of patience. This function returns the parameters of this Bernouilli-Gamma distribution which are further passed to `downscaleR.keras` function `computeRainfall` to compute the expectance of the conditional daily Gamma distributions and therefore return the amount of rainfall for a given day.\n",
    "\n",
    "We store all the predictions temporal series in 4 differenct objects: the probability of rain (`cnnss.ocu`), the no rain/rain serie (`cnnss.bin`), the amount of rain (`cnnss.amo`), and the recovered temporal serie (`cnnss`) which is the result of a multiplication between `cnnss.bin` and `cnnss.amo`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred <- lapply(1:getShape(y,\"lat\"), FUN = function(z) {\n",
    "  y <- subsetDimension(y,dimension = \"lat\",indices = z)\n",
    "  pred <- lapply(1:getShape(y,\"lon\"), FUN = function(zz) {\n",
    "    y <- subsetDimension(y,dimension = \"lon\",indices = zz) %>% gridArithmetics(0.99,operator = \"-\") %>% binaryGrid(\"GT\",0,partial = TRUE)\n",
    "    print(paste(z,\"_\",zz))\n",
    "    if (all(is.na(y$Data))) {\n",
    "      pred <- makeMultiGrid(y,y,y,y)\n",
    "      pred$Variable$varName <- c(\"bin\",\"p\",\"amo\",\"pr\")  \n",
    "    }  else {\n",
    "      inputs <- layer_input(shape = c(getShape(x,\"lat\"),getShape(x,\"lon\"),getShape(x,\"var\")))\n",
    "      l0 = inputs\n",
    "      l1 = layer_conv_2d(l0 ,filters = 50, kernel_size = c(3,3), activation = 'relu', padding = \"same\")\n",
    "      l2 = layer_conv_2d(l1,filters = 25, kernel_size = c(3,3), activation = 'relu', padding = \"same\")\n",
    "      l3 = layer_conv_2d(l2,filters = 1, kernel_size = c(3,3), activation = 'relu', padding = \"same\")\n",
    "      l4 = layer_flatten(l3)\n",
    "      parameter1 = layer_dense(l4,units = 1, activation = \"sigmoid\")\n",
    "      parameter2 = layer_dense(l4,units = 1)\n",
    "      parameter3 = layer_dense(l4,units = 1)\n",
    "      outputs = layer_concatenate(list(parameter1,parameter2,parameter3))\n",
    "      model <- keras_model(inputs = inputs, outputs = outputs)\n",
    "      pred <- downscaleCV.keras(x,y,model,\n",
    "                                folds = list(1979:1984,1985:1990,1991:1996,1997:2002,2003:2008),\n",
    "                                prepareData.keras.args = list(first.connection = \"conv\", last.connection = \"dense\", channels = \"last\"),              \n",
    "                                scaleGrid.args = list(\"type\" = \"standardize\"),\n",
    "                                compile.args = list(loss = bernouilliGammaLoss(last.connection = \"dense\"), optimizer = optimizer_adam(lr = 0.0001)),\n",
    "                                fit.args = list(batch_size = 100, epochs = 10000, validation_split = 0.1,verbose = 1,\n",
    "                                                callbacks = list(callback_early_stopping(patience = 30))),\n",
    "                                loss = \"bernouilliGammaLoss\",binarySerie = TRUE,condition = \"GE\",threshold = 1) \n",
    "      k_clear_session(); rm(model)\n",
    "      pred_amo <- computeRainfall(log_alpha = subsetGrid(pred,var = \"log_alpha\"),\n",
    "                                  log_beta = subsetGrid(pred,var = \"log_beta\"),\n",
    "                                  bias = 0.99)\n",
    "      pred_bin <- subsetGrid(pred, var = \"bin\")  \n",
    "      pred_serie <- gridArithmetics(pred_amo,pred_bin)\n",
    "      pred_amo$Variable$varName <- \"amo\"\n",
    "      pred <- makeMultiGrid(\"bin\" = pred_bin, \"pro\" = subsetGrid(pred, var = \"p\"),\"amo\" = pred_amo, \"serie\" = pred_serie)\n",
    "      pred$Dates <- makeMultiGrid(y,y,y,y)$Dates  \n",
    "    }\n",
    "    pred    \n",
    "  }) %>% bindGrid(dimension = \"lon\")    \n",
    "}) %>% bindGrid(dimension = \"lat\")\n",
    "cnnss.ocu <- subsetGrid(pred, var = \"p\")\n",
    "cnnss.bin <- subsetGrid(pred, var = \"bin\")\n",
    "cnnss.amo <- subsetGrid(pred, var = \"amo\")\n",
    "cnnss <- subsetGrid(pred, var = \"pr\")\n",
    "save(cnnss.ocu,cnnss.bin,cnnss.amo,cnnss, file = \"cnn-ss.rda\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  3.2.2 CNN multi-site (CNN-MS)\n",
    "In this section we build the CNN1 model, in a multi-site topology, only for those gridpoints located over land, as the E-OBS dataset do not contain data for sea-gridpoints. The model is stored as a `keras` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "landGP <- apply(y$Data,MARGIN = c(2,3),FUN = function(z) !anyNA(z)) %>% sum()\n",
    "inputs <- layer_input(shape = c(getShape(x,\"lat\"),getShape(x,\"lon\"),getShape(x,\"var\")))\n",
    "l0 = inputs\n",
    "l1 = layer_conv_2d(l0 ,filters = 50, kernel_size = c(3,3), activation = 'relu', padding = \"same\")\n",
    "l2 = layer_conv_2d(l1,filters = 25, kernel_size = c(3,3), activation = 'relu', padding = \"same\")\n",
    "l3 = layer_conv_2d(l2,filters = 1, kernel_size = c(3,3), activation = 'relu', padding = \"same\")\n",
    "l4 = layer_flatten(l3)\n",
    "parameter1 = layer_dense(l4,units = landGP, activation = \"sigmoid\")\n",
    "parameter2 = layer_dense(l4,units = landGP)\n",
    "parameter3 = layer_dense(l4,units = landGP)\n",
    "outputs = layer_concatenate(list(parameter1,parameter2,parameter3))\n",
    "model <- keras_model(inputs = inputs, outputs = outputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then input this model in the `downscaleCV.keras` function similar to the CNN-SS and then stored the 3 prediction temporal series in `cnnms.ocu`, `cnnms.bin` and `cnnms.amo` and `cnnms`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_rest <- gridArithmetics(y,0.99,operator = \"-\") %>% binaryGrid(\"GT\",0,partial = TRUE) \n",
    "pred <- downscaleCV.keras(x,y_rest,model,\n",
    "            folds = list(1979:1984,1985:1990,1991:1996,1997:2002,2003:2008),\n",
    "            prepareData.keras.args = list(first.connection = \"conv\", last.connection = \"dense\", channels = \"last\"),              \n",
    "            scaleGrid.args = list(\"type\" = \"standardize\"),\n",
    "            compile.args = list(loss = bernouilliGammaLoss(last.connection = \"dense\"), optimizer = optimizer_adam(lr = 0.0001)),\n",
    "            fit.args = list(batch_size = 100, epochs = 10000, validation_split = 0.1, verbose = 1,\n",
    "                                callbacks = list(callback_early_stopping(patience = 30))),\n",
    "            loss = \"bernouilliGammaLoss\",binarySerie = TRUE,condition = \"GT\",threshold = 0)  \n",
    "pred_amo <- computeRainfall(log_alpha = subsetGrid(pred,var = \"log_alpha\"),\n",
    "                            log_beta = subsetGrid(pred,var = \"log_beta\"),\n",
    "                            bias = 0.99)\n",
    "cnnms.ocu <- subsetGrid(pred, var = \"p\")\n",
    "cnnms.bin <- subsetGrid(pred, var = \"bin\")\n",
    "cnnms.amo <- pred_amo\n",
    "cnnms <- gridArithmetics(cnnms.amo,cnnms.bin)\n",
    "save(cnnms.ocu,cnnms.bin,cnnms.amo,cnnms, file = \"cnn-ms.rda\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Validation of the Results\n",
    "In this figure, we calculate the validation indices by using the library `climate4R.value` of `climate4R`. In particular the indices used are: the Roc Skill Score (ROCSS), the Root Mean Squared Error (RMSE), the spearman correlation and the relative biases of the climatology, and are used for each of the folds independently."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "[2021-01-13 09:55:55] Done.\n",
      "\n",
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      "\n",
      "[2021-01-13 09:55:59] Done.\n",
      "\n",
      "[2021-01-13 09:55:59] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:56:02] Done.\n",
      "\n",
      "[2021-01-13 09:56:03] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:56:04] Done.\n",
      "\n",
      "[2021-01-13 09:56:05] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:56:06] Done.\n",
      "\n",
      "[2021-01-13 09:56:06] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:56:07] Done.\n",
      "\n",
      "[2021-01-13 09:56:08] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:56:09] Done.\n",
      "\n",
      "[2021-01-13 09:56:09] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:56:11] Done.\n",
      "\n",
      "[2021-01-13 09:56:12] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:56:13] Done.\n",
      "\n",
      "[2021-01-13 09:56:14] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:56:15] Done.\n",
      "\n",
      "[2021-01-13 09:56:16] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:56:17] Done.\n",
      "\n",
      "[2021-01-13 09:56:17] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:56:18] Done.\n",
      "\n",
      "[2021-01-13 09:56:18] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:56:19] Done.\n",
      "\n",
      "[2021-01-13 09:56:19] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:56:20] Done.\n",
      "\n",
      "[2021-01-13 09:56:20] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:56:21] Done.\n",
      "\n",
      "[2021-01-13 09:56:21] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:56:22] Done.\n",
      "\n",
      "[2021-01-13 09:56:22] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:56:23] Done.\n",
      "\n",
      "[2021-01-13 09:56:23] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:56:24] Done.\n",
      "\n",
      "[2021-01-13 09:56:24] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:56:25] Done.\n",
      "\n",
      "[2021-01-13 09:56:25] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:56:26] Done.\n",
      "\n",
      "[2021-01-13 09:56:26] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:56:27] Done.\n",
      "\n",
      "[2021-01-13 09:56:28] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:56:28] Done.\n",
      "\n",
      "[2021-01-13 09:56:28] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:56:29] Done.\n",
      "\n",
      "[2021-01-13 09:56:30] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:56:32] Done.\n",
      "\n",
      "[2021-01-13 09:56:33] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:56:36] Done.\n",
      "\n",
      "[2021-01-13 09:56:37] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:56:40] Done.\n",
      "\n",
      "[2021-01-13 09:56:40] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:56:43] Done.\n",
      "\n",
      "[2021-01-13 09:56:44] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:56:45] Done.\n",
      "\n",
      "[2021-01-13 09:56:45] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:56:46] Done.\n",
      "\n",
      "[2021-01-13 09:56:47] Computing member 1 out of 1\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2021-01-13 09:56:48] Done.\n",
      "\n",
      "[2021-01-13 09:56:49] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:56:50] Done.\n",
      "\n",
      "[2021-01-13 09:56:50] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:56:52] Done.\n",
      "\n",
      "[2021-01-13 09:56:53] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:56:54] Done.\n",
      "\n",
      "[2021-01-13 09:56:55] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:56:56] Done.\n",
      "\n",
      "[2021-01-13 09:56:57] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:56:58] Done.\n",
      "\n",
      "[2021-01-13 09:56:59] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:57:00] Done.\n",
      "\n",
      "[2021-01-13 09:57:00] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:57:02] Done.\n",
      "\n",
      "[2021-01-13 09:57:02] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:57:03] Done.\n",
      "\n",
      "[2021-01-13 09:57:03] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:57:04] Done.\n",
      "\n",
      "[2021-01-13 09:57:04] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:57:05] Done.\n",
      "\n",
      "[2021-01-13 09:57:05] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:57:06] Done.\n",
      "\n",
      "[2021-01-13 09:57:07] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:57:08] Done.\n",
      "\n",
      "[2021-01-13 09:57:08] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:57:09] Done.\n",
      "\n",
      "[2021-01-13 09:57:09] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:57:10] Done.\n",
      "\n",
      "[2021-01-13 09:57:10] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:57:11] Done.\n",
      "\n",
      "[2021-01-13 09:57:11] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:57:12] Done.\n",
      "\n",
      "[2021-01-13 09:57:12] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 09:57:13] Done.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_bin <- binaryGrid(y, condition = \"GT\", threshold = 1)\n",
    "rocss.ana <- rocss.glm <- rocss.cnnss <- rocss.cnnms <- vector(mode = \"numeric\", length = length(folds))\n",
    "rmse.ana <- rmse.glm <- rmse.cnnss <- rmse.cnnms <- vector(mode = \"numeric\", length = length(folds))\n",
    "corr.ana <- corr.glm <- corr.cnnss <- corr.cnnms <- vector(mode = \"numeric\", length = length(folds))\n",
    "bias.ana <- bias.glm <- bias.cnnss <- bias.cnnms <- vector(mode = \"numeric\", length = length(folds))\n",
    "for (i in 1:length(folds)) {\n",
    "  rocss.ana[i] <- valueMeasure(subsetGrid(y_bin,years = folds[[i]]),\n",
    "                               subsetGrid(ana,years = folds[[i]]),\n",
    "                               measure.code=\"ts.rocss\")$Measure$Data %>% mean(na.rm = TRUE) %>%\n",
    "    round(digits = 2)\n",
    "  rocss.glm[i] <- valueMeasure(subsetGrid(y_bin,years = folds[[i]]),\n",
    "                               subsetGrid(glm.ocu,years = folds[[i]]),\n",
    "                               measure.code=\"ts.rocss\")$Measure$Data %>% mean(na.rm = TRUE) %>%\n",
    "    round(digits = 2)\n",
    "  rocss.cnnss[i] <- valueMeasure(subsetGrid(y_bin,years = folds[[i]]),\n",
    "                                subsetGrid(cnnss.ocu,years = folds[[i]]),\n",
    "                                measure.code=\"ts.rocss\")$Measure$Data %>% mean(na.rm = TRUE) %>%\n",
    "    round(digits = 2)\n",
    "  rocss.cnnms[i] <- valueMeasure(subsetGrid(y_bin,years = folds[[i]]),\n",
    "                                subsetGrid(cnnms.ocu,years = folds[[i]]),\n",
    "                                measure.code=\"ts.rocss\")$Measure$Data %>% mean(na.rm = TRUE) %>%\n",
    "    round(digits = 2)\n",
    "  \n",
    "  rmse.ana[i] <- valueMeasure(subsetGrid(y,years = folds[[i]]),\n",
    "                              subsetGrid(ana,years = folds[[i]]),\n",
    "                              measure.code=\"ts.RMSE\")$Measure$Data %>% mean(na.rm = TRUE) %>%\n",
    "    round(digits = 2)\n",
    "  rmse.glm[i] <- valueMeasure(subsetGrid(y,years = folds[[i]]),\n",
    "                              subsetGrid(glm.amo,years = folds[[i]]),\n",
    "                              measure.code=\"ts.RMSE\")$Measure$Data %>% mean(na.rm = TRUE) %>%\n",
    "    round(digits = 2)\n",
    "  rmse.cnnss[i] <- valueMeasure(subsetGrid(y,years = folds[[i]]),\n",
    "                               subsetGrid(cnnss.amo,years = folds[[i]]),\n",
    "                               measure.code=\"ts.RMSE\")$Measure$Data %>% mean(na.rm = TRUE) %>%\n",
    "    round(digits = 2)\n",
    "  rmse.cnnms[i] <- valueMeasure(subsetGrid(y,years = folds[[i]]),\n",
    "                               subsetGrid(cnnms.amo,years = folds[[i]]),\n",
    "                               measure.code=\"ts.RMSE\")$Measure$Data %>% mean(na.rm = TRUE) %>%\n",
    "    round(digits = 2)\n",
    "  \n",
    "  corr.ana[i] <- valueMeasure(subsetGrid(y,years = folds[[i]]),\n",
    "                              subsetGrid(ana,years = folds[[i]]),\n",
    "                              measure.code=\"ts.rs\")$Measure$Data %>% mean(na.rm = TRUE) %>%\n",
    "    round(digits = 2)\n",
    "  corr.glm[i] <- valueMeasure(subsetGrid(y,years = folds[[i]]),\n",
    "                              subsetGrid(glm,years = folds[[i]]),\n",
    "                              measure.code=\"ts.rs\")$Measure$Data %>% mean(na.rm = TRUE) %>%\n",
    "    round(digits = 2)\n",
    "  corr.cnnss[i] <- valueMeasure(subsetGrid(y,years = folds[[i]]),\n",
    "                               subsetGrid(cnnss,years = folds[[i]]),\n",
    "                               measure.code=\"ts.rs\")$Measure$Data %>% mean(na.rm = TRUE) %>%\n",
    "    round(digits = 2)\n",
    "  corr.cnnms[i] <- valueMeasure(subsetGrid(y,years = folds[[i]]),\n",
    "                               subsetGrid(cnnms,years = folds[[i]]),\n",
    "                               measure.code=\"ts.rs\")$Measure$Data %>% mean(na.rm = TRUE) %>%\n",
    "    round(digits = 2)\n",
    "  \n",
    "  bias.ana[i] <- valueMeasure(subsetGrid(y,years = folds[[i]]), \n",
    "                              subsetGrid(ana,years = folds[[i]]), \n",
    "                              measure.code=\"biasRel\",index.code=\"Mean\")$Measure$Data %>% \n",
    "    mean(na.rm = TRUE) %>% \n",
    "    round(digits = 2) \n",
    "  bias.glm[i] <- valueMeasure(subsetGrid(y,years = folds[[i]]), \n",
    "                              subsetGrid(glm,years = folds[[i]]), \n",
    "                              measure.code=\"biasRel\",index.code=\"Mean\")$Measure$Data %>% \n",
    "    mean(na.rm = TRUE) %>% \n",
    "    round(digits = 2) \n",
    "  bias.cnnss[i] <- valueMeasure(subsetGrid(y,years = folds[[i]]), \n",
    "                               subsetGrid(cnnss,years = folds[[i]]), \n",
    "                               measure.code=\"biasRel\",index.code=\"Mean\")$Measure$Data %>% \n",
    "    mean(na.rm = TRUE) %>% \n",
    "    round(digits = 2) \n",
    "  bias.cnnms[i] <- valueMeasure(subsetGrid(y,years = folds[[i]]), \n",
    "                               subsetGrid(cnnms,years = folds[[i]]), \n",
    "                               measure.code=\"biasRel\",index.code=\"Mean\")$Measure$Data %>% \n",
    "    mean(na.rm = TRUE) %>% \n",
    "    round(digits = 2) \n",
    "} "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can easily plot the results using the base R function `plot`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2021-01-13 11:05:02] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 11:05:03] Done.\n",
      "\n",
      "[2021-01-13 11:05:04] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 11:05:06] Done.\n",
      "\n",
      "[2021-01-13 11:05:07] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 11:05:08] Done.\n",
      "\n",
      "[2021-01-13 11:05:09] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 11:05:11] Done.\n",
      "\n",
      "[2021-01-13 11:05:12] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 11:05:13] Done.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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ddfh4aGOrVGAAAAANfFdnYB9yxevLio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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "par(mfrow = c(2,2))\n",
    "# ROCSS\n",
    "plot(rocss.ana, type = \"l\", col = \"black\", xlab = \"Folds\", ylab = \"ROCSS\", ylim = c(0,1))\n",
    "lines(rocss.glm, col = \"red\")\n",
    "lines(rocss.cnnss, col = \"blue\")\n",
    "lines(rocss.cnnms, col = \"green\")\n",
    "legend(\"bottomright\", legend = c(\"ANA\",\"GLM\",\"CNNSS\",\"CNNMS\"), col = c(\"black\",\"red\",\"blue\",\"green\"), lty = c(1,1,1,1))\n",
    "\n",
    "# RMSE\n",
    "plot(rmse.ana, type = \"l\", col = \"black\", xlab = \"Folds\", ylab = \"RMSE (mm/day)\", ylim = c(1,10))\n",
    "lines(rmse.glm, col = \"red\")\n",
    "lines(rmse.cnnms, col = \"green\")\n",
    "rmse.cnnssMasked <- c()\n",
    "for (i in 1:length(folds)) {\n",
    "    rmse <- valueMeasure(subsetGrid(y,years = folds[[i]]),subsetGrid(cnnss.amo,years = folds[[i]]),measure.code=\"ts.RMSE\")$Measure$Data\n",
    "    rmse.cnnssMasked[i] <- rmse[which(rmse <= 10, arr.ind = TRUE)] %>% mean(na.rm = TRUE) %>% round(digits = 2) \n",
    "}\n",
    "lines(rmse.cnnssMasked, col = \"blue\")\n",
    "\n",
    "# CORRELATION\n",
    "plot(corr.ana, type = \"l\", col = \"black\", xlab = \"Folds\", ylab = \"Correlation\", ylim = c(0.5,0.8))\n",
    "lines(corr.glm, col = \"red\")\n",
    "lines(corr.cnnss, col = \"blue\")\n",
    "lines(corr.cnnms, col = \"green\")\n",
    "\n",
    "# Relative bias\n",
    "plot(bias.ana, type = \"l\", col = \"black\", xlab = \"Folds\", ylab = \"Rel. Bias (%)\", ylim = c(-0.5,0.5))\n",
    "lines(bias.glm, col = \"red\")\n",
    "lines(bias.cnnss, col = \"blue\")\n",
    "lines(bias.cnnms, col = \"green\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see very high RMSE for the CNN-SS model (>100 mm/day), if we do not mask certain gridpoints. We can take a deeper look at the results by analyzing the number of gridpoints with extremely high RMSE. These gridpoints would have suffered overfitting whereas multi-task architectures solve this instability for these gridpoints."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2021-01-13 10:40:10] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 10:40:13] Done.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"The fraction of gridpoints with an unstable behaviour is: 5%\"\n"
     ]
    }
   ],
   "source": [
    "rmse <- valueMeasure(y,cnnss.amo,measure.code=\"ts.RMSE\")$Measure$Data %>% round(digits = 2)\n",
    "print(paste0(\"The fraction of gridpoints with an unstable behaviour is: \", round(sum(rmse > 10,na.rm = TRUE)/sum(!is.na(rmse),na.rm = TRUE), digits = 2)*100, \"%\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can visualize where these 5% of the gridpoints are located by calling the function `spatialPlot`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2021-01-13 10:58:24] Computing member 1 out of 1\n",
      "\n",
      "[2021-01-13 10:58:27] Done.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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ccBAABnYavNdzqdFDs/Um0rts6f/vSn55577sgv/6iuzDr/0QAAoMHZavOaN28e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\nQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rmse <- valueMeasure(y,cnnss.amo,measure.code=\"ts.RMSE\")$Measure\n",
    "rmse$Data[which(rmse$Data <= 10, arr.ind = TRUE)] <- 1\n",
    "rmse$Data[which(rmse$Data > 10, arr.ind = TRUE)] <- 0\n",
    "attr(rmse$Data,\"dimensions\") <- c(\"lat\",\"lon\")\n",
    "spatialPlot(rmse, backdrop.theme = \"coastline\", at = seq(0,1,length.out = 3), set.min = 0, set.max = 1)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
